A three stage ‘Matching-Retrieval-Optimization’ method for radar–rainfall retrieval: a case study in the Yiluo River Basin, China

In modern rainfall monitoring, forecasting, and early warning systems, radar serves as a critical technological foundation, profoundly transforming approaches to flood management and disaster mitigation. To improve the accuracy of radar-based rainfall retrievals and effectively assess the reliability of the results, this study introduces an innovative, transferable three-stage radar–rainfall retrieval framework (TS-RR), consisting of three essential components: matching, retrieval, and optimization. Additionally, a novel point-real-process (PAP) three-dimensional evaluation method is proposed, enabling a comprehensive and quantitative assessment of retrieval accuracy from three perspectives: stations, spatial distribution, and rainfall processes. The results demonstrate that (1) the TS-RR framework effectively guides radar–rainfall retrieval, with the optimization phase (Stage III) significantly enhancing the capture of rainfall peaks, improving spatial distribution accuracy (KLD 0.9). (2) Under limited sample conditions, the PMM in Stage II exhibits strong robustness, maintaining stable correlation performance (correlation coefficient, CC = 0.7–0.9). (3) The PAP approach effectively evaluates the reliability of the retrieval results. The proposed method offers practical value by providing scientifically grounded data support and decision-making references for water resource management, flood forecasting, and early warning systems.